基础模型的不确定性意识组合使质母细胞瘤与其模仿者区别开来
Junhan Zhao1,2, Shih-Yen Lin1, Raphaël Attias1
1Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
Nature communications
|September 29, 2025
概括
一个新的AI工具,PICTURE,从病理幻灯片准确诊断中枢神经系统癌症,如质母细胞瘤和淋巴瘤. 它还可以识别罕见的癌症类型,帮助个性化治疗决策.
科学领域:
- 计算病理学计算病理学
- 人工智能在瘤学中的应用
- 神经瘤诊断诊断神经瘤诊断
背景情况:
- 准确的病理诊断对于个性化治疗中枢神经系统 (CNS) 癌症至关重要.
- 由于重叠的特征,区分质母细胞瘤和初级中枢神经系统淋巴瘤是很困难的,需要不同的治疗.
研究的目的:
- 开发一个AI系统,PICTURE,用于准确和快速的中枢神经系统癌症的病理诊断.
- 解决中枢神经系统瘤学中病理模仿所带来的诊断挑战.
主要方法:
- 利用了全球2141个病理学幻灯片来训练PICTURE系统.
- 采用贝叶斯推理,深层集合和规范化流程来量化不确定性.
- 为区分罕见的中枢神经系统癌症类型开发了一个可通用的框架.
主要成果:
- 在诊断质母细胞瘤和初级中枢神经系统淋巴瘤方面,PICTURE获得了0.989的AUROC.
- 在五个独立队列中进行的验证显示出高性能 (AUROC 0.924-0.996).
- 图片成功识别了67种罕见的中枢神经系统癌症类型的样本,除了结质瘤和淋巴瘤.
结论:
- PICTURE系统为挑战性中枢神经系统癌症诊断提供了一个强大的解决方案.
- 这种人工智能框架能够快速准确地区分病理模仿.
- 图片为中枢神经系统癌症患者提供及时和个性化的治疗策略.
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